Quiz 3

library(tidyverse)
library(openintro)
library(readxl)
library(plotly)
library("rnaturalearth")
library("rnaturalearthdata")

my_map_theme <- function(){
  theme(panel.background=element_blank(),
        axis.text=element_blank(),
        axis.ticks=element_blank(),
        axis.title=element_blank())
}

Question 1

WHOLifeExpectancy <- read_excel("C:/RLibrary/Quiz 3 Andrew McLain/Andrew McLain Quiz 3/WHOLifeExpectancy.xlsx", 
    col_types = c("text", "numeric", "numeric", 
        "numeric", "numeric", "numeric", "numeric", "numeric", 
        "numeric", "numeric", "numeric", "numeric", "numeric", 
        "numeric", "numeric", "numeric", "numeric", "numeric", 
        "numeric", "numeric", "numeric", "numeric", "numeric", 
        "numeric", "numeric", "numeric", "numeric", "numeric", 
        "numeric", "numeric", "numeric"), skip = 2)

names(WHOLifeExpectancy) <- c("country",
                              "HALEbirth_Both sexes_2016", 
                              "HALEbirth_Both sexes_2015",
                              "HALEbirth_Both sexes_2010",
                              "HALEbirth_Both sexes_2005",
                              "HALEbirth_Both sexes_2000",
                              "HALEbirth_Male_2016",
                              "HALEbirth_Male_2015", 
                              "HALEbirth_Male_2010",
                              "HALEbirth_Male_2005",
                              "HALEbirth_Male_2000",
                              "HALEbirth_Female_2016",
                              "HALEbirth_Female_2015",
                              "HALEbirth_Female_2010", 
                              "HALEbirth_Female_2005",
                              "HALEbirth_Female_2000",
                              "HALE60_Both sexes_2016", 
                              "HALE60_Both sexes_2015",
                              "HALE60_Both sexes_2010",
                              "HALE60_Both sexes_2005", 
                              "HALE60_Both sexes_2000",
                              "HALE60_Male_2016",
                              "HALE60_Male_2015", 
                              "HALE60_Male_2010",
                              "HALE60_Male_2005",
                              "HALE60_Male_2000",
                              "HALE60_Female_2016",
                              "HALE60_Female_2015",
                              "HALE60_Female_2010", 
                              "HALE60_Female_2005",
                              "HALE60_Female_2000")

Question 2

WHOLifeExpectancy <- WHOLifeExpectancy %>% 
                      pivot_longer(c(
                              "HALEbirth_Both sexes_2016", 
                              "HALEbirth_Both sexes_2015",
                              "HALEbirth_Both sexes_2010",
                              "HALEbirth_Both sexes_2005",
                              "HALEbirth_Both sexes_2000",
                              "HALEbirth_Male_2016",
                              "HALEbirth_Male_2015", 
                              "HALEbirth_Male_2010",
                              "HALEbirth_Male_2005",
                              "HALEbirth_Male_2000",
                              "HALEbirth_Female_2016",
                              "HALEbirth_Female_2015",
                              "HALEbirth_Female_2010", 
                              "HALEbirth_Female_2005",
                              "HALEbirth_Female_2000",
                              "HALE60_Both sexes_2016", 
                              "HALE60_Both sexes_2015",
                              "HALE60_Both sexes_2010",
                              "HALE60_Both sexes_2005", 
                              "HALE60_Both sexes_2000",
                              "HALE60_Male_2016",
                              "HALE60_Male_2015", 
                              "HALE60_Male_2010",
                              "HALE60_Male_2005",
                              "HALE60_Male_2000",
                              "HALE60_Female_2016",
                              "HALE60_Female_2015",
                              "HALE60_Female_2010", 
                              "HALE60_Female_2005",
                              "HALE60_Female_2000"),
                              names_to = 
                                "names",
                              values_to = 
                                "values") %>%
                          separate(names,
                                   c("category", 
                                     "sex",
                                     "year"),
                                   sep="_") %>%
                          pivot_wider(
                            names_from=category,
                            values_from=values
                          ) %>%
                          arrange(desc(year),
                                  country)
head(WHOLifeExpectancy, 10)

## # A tibble: 10 x 5
##    country     sex        year  HALEbirth HALE60
##    <chr>       <chr>      <chr>     <dbl>  <dbl>
##  1 Afghanistan Both sexes 2016       53     11.3
##  2 Afghanistan Male       2016       52.1   10.9
##  3 Afghanistan Female     2016       54.1   11.7
##  4 Albania     Both sexes 2016       68.1   16.3
##  5 Albania     Male       2016       66.7   15.3
##  6 Albania     Female     2016       69.6   17.4
##  7 Algeria     Both sexes 2016       65.5   15.8
##  8 Algeria     Male       2016       65.4   15.7
##  9 Algeria     Female     2016       65.6   15.8
## 10 Angola      Both sexes 2016       55.8   13.6

Question 3

world <- ne_countries(scale = "medium", returnclass = "sf")

WHOLifeExpectancy2 <- WHOLifeExpectancy %>%
                      filter(sex == "Both sexes",
                             year == "2016")

WHO_metadata <- read_csv("C:/RLibrary/Quiz 3 Andrew McLain/Andrew McLain Quiz 3/WHO_metadata.csv",
                         col_names=c("x",
                                     "ISO",
                                     "country")) %>%
                         select("ISO", "country")

WHOLifeExpectancy2_metadata <- WHOLifeExpectancy2 %>%
                               left_join(
                                 WHO_metadata,
                                 c("country"=
                                   "country"
                                 )
                               )

world_WHOLifeExpectancy2_metadata <- world %>%
                                left_join(
                                WHOLifeExpectancy2_metadata,
                                c("iso_a3"=
                                  "ISO")
                                )

Question 4

plot_world_WHOLifeExpectancy2_metadata <-
  ggplot(world_WHOLifeExpectancy2_metadata) +
  geom_sf(aes(fill=HALEbirth)) +
  my_map_theme() +
  labs(title = "Healthy Life Expectancy
                at Birth in 2016") +
  scale_fill_continuous("Healthy Life\nExpectancy\nat Birth (years)",
                        low="red", 
                        high="purple")

plot_world_WHOLifeExpectancy2_metadata

Question 5

world_WHOLifeExpectancy2_metadata <-
  world_WHOLifeExpectancy2_metadata %>%
  mutate(text = paste("<b>",country,"</b>\nHealthy Life Expectancy:",HALEbirth)) 

plot2_world_WHOLifeExpectancy2_metadata <-
  ggplot(world_WHOLifeExpectancy2_metadata) +
  geom_sf(aes(fill=HALEbirth
              +runif(nrow(
                world_WHOLifeExpectancy2_metadata)),
              text=text
              )) +
  my_map_theme() +
  scale_fill_continuous("Healthy Life\nExpectancy\nat Birth (years)",
                        low="red", 
                        high="purple")  


ggplotly(plot2_world_WHOLifeExpectancy2_metadata, 
         tooltip = "text") %>%
  style(hoveron = "fills")